Videos underlying the publication: A Novel MPC Formulation for Dynamic Target Tracking with Increased Area Coverage for Search-and-Rescue Robots

This dataset contains the videos of the trajectories of a robot and victims in a simulated search-and-rescue scenario, the videos of experiments performed with robots in real life, and the tables with the uncertainty values used in the simulations.<br>The videos of the trajectories of a robot and victims in a simulated search-and-rescue scenario consider five different approaches for comparison purposes: our <strong>tube-based Model Predictive Control</strong> (MPC) approach; a <strong>Farrohksiar tube-based MPC</strong> approach; an <strong>A*-MPC</strong> approach; <strong>randomized MPC</strong> approach; and a <strong>Boustrophedon-motion-A*</strong> approach. The scenario consisted on a disaster building in which the robot has to explore the environment to detect 3 victims and avoid 5 static obstacles, and finally go to the exit point, while the victims move accordingly to an established crowd evacuation model.<br>The videos of experiments of our <strong>tube-based Model Predictive Control</strong> (MPC) approach with robots in real life consist of three scenarios in a lab environment, with a TurtleBot 3 Burger robot behaving as the search-and-rescue robot, an iRobot Create 3 robot behaving as the victim, and 3 static obstacles.<br>The dataset also contains the values of the uncertainties, i.e., the non-smoothness map values used for x and y coordinates.

本数据集收录三类内容：一是模拟搜救场景下机器人与遇险者运动轨迹的视频，二是真实机器人搜救实验的录像，三是仿真过程中所用不确定性数值的表格。
其中模拟搜救场景的轨迹视频涵盖5种对比研究方法，分别为本文提出的基于管状模型预测控制（tube-based Model Predictive Control，MPC）方法、Farrokhsiar管状模型预测控制方法、A*-MPC方法、随机化模型预测控制（randomized MPC）方法，以及牛耕式运动A*（Boustrophedon-motion-A*）方法。该模拟场景设置为一座受灾建筑，机器人需对环境进行探索，以探测3名遇险者、避开5处静态障碍物，并最终抵达出口点；同时遇险者将按照既定的人群疏散模型进行移动。
本数据集收录的真实机器人实验录像均采用我们提出的基于管状模型预测控制方法，涵盖实验室环境下的3种场景。实验中以TurtleBot 3 Burger机器人作为搜救机器人，iRobot Create 3机器人模拟遇险者，并设置3处静态障碍物。
本数据集还包含不确定性数值，即用于x、y坐标的非平滑度映射数值。